Adaptive Unscented Kalman Filter Algorithm Research Articles

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries’ parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm.

Vehicle State Estimation Based on Sage–Husa Adaptive Unscented Kalman Filtering

To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is derived by establishing the vehicle’s three degrees-of-freedom (DOF) model. On this basis, the Sage–Husa algorithm and unscented Kalman filter (UKF) are combined to form the AUKF algorithm to adaptively update the statistical feature estimation of measurement noise. Finally, a co-simulation using Carsim and Matlab/Simulink confirms the algorithm is effective and reasonable. The simulation results demonstrate that the proposed algorithm, compared with the UKF algorithm, increases estimation accuracy by 19.13%, 32.8%, and 39.46% in yaw rate, side-slip angle, and longitudinal velocity, respectively. This is because the proposed algorithm adaptively adjusts the measurement noise covariance matrix, which can estimate the state parameters of the vehicle more accurately.

Initial value compensation of an adaptive fractional-order unscented Kalman filter for estimating the state of charge of lithium-ion batteries

This study indicates an adaptive fractional-order unscented Kalman filter (UKF) with unknown parameters and order, which provides a higher accuracy of SOC estimation. In order to solve the problem of reduced estimation accuracy caused by the low order in the adaptive estimation, this paper adopts the augmented vector method for the initial value compensation (IVC) to reduce the impact of initial values on the estimation accuracy. Because the parameters of the LIB change with the charge and discharge of the battery use and the working conditions, and to increase the ability of the adaptive SOC estimation, this paper studies an adaptive unscented Kalman filter (AUKF) algorithm for the joint estimation of state, parameters, and order of model based on IVC. The algorithm can achieve the adaptive estimation of SOC under different working conditions. Finally, several sets of experiments are conducted under different working conditions to verify that the AUKF algorithm with IVC can achieve the effective estimation of SOC, and the adaptive effect and the estimation error of IVC on the algorithm are better than those without IVC.

Power Scheduling Optimization Method of Wind-Hydrogen Integrated Energy System Based on the Improved AUKF Algorithm

With the proposal of China’s green energy strategy, the research and development technologies of green energy such as wind energy and hydrogen energy are becoming more and more mature. However, the phenomenon of wind abandonment and anti-peak shaving characteristics of wind turbines have a great impact on the utilization of wind energy. Therefore, this study firstly builds a distributed wind-hydrogen hybrid energy system model, then proposes the power dispatching optimization technology of a wind-hydrogen integrated energy system. On this basis, a power allocation method based on the AUKF (adaptive unscented Kalman filter) algorithm is proposed. The experiment shows that the power allocation strategy based on the AUKF algorithm can effectively reduce the incidence of battery overcharge and overdischarge. Moreover, it can effectively deal with rapid changes in wind speed. The wind hydrogen integrated energy system proposed in this study is one of the important topics of renewable clean energy technology innovation. Its grid-connected power is stable, with good controllability, and the DC bus is more secure and stable. Compared with previous studies, the system developed in this study has effectively reduced the ratio of abandoned air and its performance is significantly better than the system with separate grid connected fans and single hydrogen energy storage. It is hoped that this research can provide some solutions for the research work on power dispatching optimization of energy systems.

A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter

The state of lithium-ion battery is a key indicator for the battery management system (BMS) of electric vehicles (EVs). State of charge (SOC) and state of health (SOH) of the power cell are the main parameters of the BMS during operation. In this paper, an adaptive unscented Kalman filter algorithm (AUKF) is presented for the joint estimation of SOC and SOH of lithium-ion batteries. Firstly, this paper develops a 2-RC equivalent circuit model and identifies the model parameters using recursive least squares algorithm with forgetting factor. Then, the SOC and SOH of the battery are estimated simultaneously by AUKF. Finally, the accuracy of the proposed method is verified under different operating conditions. The experiment results show that the maximum SOC estimation error is under 0.08% by the proposed method. Compared with the unscented Kalman filtering (UKF), it is shown that the proposed method is more accurate and reliable. An effective method is provided for state estimation for battery management system.

Fractional variable-order calculus based state of charge estimation of Li-ion battery using dual fractional order Kalman filter

Although fractional order based models and approaches have been proposed to determine battery key state characteristics, the inflexible order has a detrimental impact on battery modeling and SOC calculation. In this study, a novel SOC estimation strategy based on the combination of simplified fractional variable-order equivalent circuit model (FVOECM) and FVO adaptive dual Kalman filter (FVOADKF) algorithm is proposed. The simplified FVOECM is designed to represent the battery's dynamic external electrical characteristics. The FVO-based adaptive extended Kalman filter algorithm is utilized for the battery SOC estimation, while the FVO-based adaptive unscented Kalman filter algorithm is used to determine the order of the FVOECM. Consequently, the updated order effectively tracks the transaction of the battery FVOECM thus improving the SOC estimation accuracy. Furthermore, the influences of SOC initialization error and FO backtracking length on the algorithm convergence are analyzed. The effectiveness of the FVOECM is confirmed under two different conditions, and four experiment results indicate that the effectiveness of the FVOADKF is superior to other estimation methods.

Estimation of Vehicle State Based on IMM-AUKF

Establishing a symmetrical model of surrounding vehicles and accurately obtaining the driving state of the surrounding vehicles in the driving environment can improve the safety of driving, which is an important issue that needs to be considered in the automatic driving system or auxiliary driving system. Therefore, we propose an adaptive unscented Kalman filter algorithm based on Interacting Multiple Model (IMM) theory to estimate the state of target vehicle in the high-speed driving environment. To be specific, we use the Constant Turn Rate and Acceleration (CTRA) theory to establish the target vehicle kinematics model, simultaneously, in order to overcome the problem of estimator failure when the yaw rate is close to zero, a simplified version of the CTRA model is also introduced into the estimation process. In addition, the parameter adaptation strategy is added, so the proposed estimator can overcome the uncertainty of the noise model and improve its accuracy. Finally, the effectiveness of proposed state estimation algorithm is verified on the Carsim and Simulink co-simulation platform. The results of simulations and experiments show that the accuracy and stability of IMM-based algorithm is better than the single-model algorithm in different scenarios, and the parameter adaptation strategy brings performance improvement.

Application of Adaptive UKF Algorithm in Multi-target Tracking and Positioning System

Adaptive filtering algorithm (FIR) is a design method of adaptive variable target tracking system based on probability density distribution model. The algorithm realizes the target movement in the global range by estimating the parameters of different regions in the image, which improves the real-time performance and effectiveness.

New Artificial Intelligence Technology Applied in Automobile Lithium Battery Manufacturing

In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle power batteries, this paper is based on artificial intelligence technology for lithium-ion battery model and parameter identification algorithm, adaptive unscented Kalman filter algorithm and SOC estimation based on battery model fusion Algorithm for research. The simulation results show that the SOC error estimated by the artificial intelligence adaptive Kalman filter method is less than 2.4%, which effectively reduces the impact of unknown interference noise on the battery management system when the electric vehicle is driving. The SOC estimation accuracy is higher than that of the extended Kalman method, and has good robustness.

State of charge estimation with the adaptive unscented Kalman filter based on an accurate equivalent circuit model

The accurate estimation of the state of charge (SOC) plays an important role in optimizing the energy management of electric vehicles. To improve the estimation accuracy of SOC, this study proposes a SOC estimation method with the adaptive unscented Kalman filter (AUKF) based on an accurate equivalent circuit model. First, combined with the n-RC equivalent circuit model, the relationship curve between SOC and open-circuit voltage (OCV) is fitted via parameter identification. Next, the accuracy advantage of the n-RC model is proven by comparing and analyzing the voltage response curves of different n-RC models and several common equivalent circuit models. Moreover, the accuracy of the n-RC model becomes higher with the increasing n. Then the numerical validation experiments are established based on the AUKF algorithm, and the constant current discharge experiment, hybrid pulse experiment, robustness verification experiment are carried out. Finally, to effectively evaluate this estimation approach, in addition to setting up a control group experiment based on the extended Kalman filter (EKF) algorithm and unscented Kalman filter (UKF) algorithm. The experiment results show that, compared with the other two algorithms, the SOC estimation method based on AUKF is more accurate.

A novel battery state estimation model based on unscented Kalman filter

Accurate estimation of the state of charge (SOC) of batteries is very important for real-time monitoring and safety control of electric vehicles. Four aspects of efforts are applied to promote the accuracy of SOC estimation. Firstly, the state-space equation of the battery model based on the Thevenin model is established and the parameters of the model are identified by the forgetting factor recursive least square method. Secondly, aiming at the nonlinear relationship between the open-circuit voltage (OCV) and SOC, the least square support vector machine is proposed to establish the mapping relationship between OCV and SOC. Thirdly, the influence of fitting accuracy of the OCV-SOC curve on SOC estimation is analyzed. Based on this, an error model is proposed, and a joint estimator using an adaptive unscented Kalman filter algorithm combining the error model is proposed. Finally, compared with the estimated SOC results of the traditional SOC estimation method, the experimental results show that the proposed model has better estimation ability and robustness.

A new multi-sensor hierarchical data fusion algorithm based on unscented Kalman filter for the attitude observation of the wave glider

In order to improve the accuracy of a single low-cost attitude observation sensor of the wave glider (WG), a new multi-sensor hierarchical data fusion algorithm is proposed. The bottom-level fusion strategy is the complementary data fusion of the accelerometer and the magnetometer, and the global optimal estimation of the bottom-level fusion result and the gyroscope based on the unscented Kalman filter (UKF) algorithm in the top-level fusion strategy. However, the estimation accuracy of the standard UKF algorithm depends on the accuracy of the noise model. Inaccurate estimation of the covariance matrix of the state noise and the measurement noise will cause the accuracy of the UKF algorithm to decrease or even diverge. By introducing the covariance matching technology, the innovation sequence and the residual sequence are used to realize the real-time estimation of the covariance matrix of state noise and measurement noise. At the same time, in view of the characteristic that the measurement equation is a strongly nonlinear equation, an improved covariance matching adaptive UKF algorithm is proposed. It effectively overcomes the limitations of the standard UKF and improves the adaptability of the multi-sensor hierarchical data fusion algorithm. Numerical simulation and experiment are carried out and the results demonstrate that the multi-sensor hierarchical data fusion algorithm is effective in solving the precision problem of the WG low-cost attitude observation unit.

Estimation of the State of Charge of Lithium Batteries Based on Adaptive Unscented Kalman Filter Algorithm

The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.

State estimation of distributed electric vehicle based on robust adaptive UKF

The key to achieving active safety and automatic driving is accurate parameters of the vehicle state. The unscented Kalman filter (UKF) algorithm will seriously affect the vehicle state’s estimation accuracy if the noise of measurement is high or the covariance of noise does not suit. A robust adaptive UKF algorithm based on fault detection mechanism is proposed to estimate the state of distributed drive electric vehicle. The algorithm uses the residual vector of observation noise to identify whether there is a fault in the system, determines whether the observation noise covariance and the process noise covariance need to be adjusted adaptively according to the statistical function, and updates the covariance based on the weight factors. A robust adaptive UKF estimator is designed to estimate three important state variables: longitudinal speed, lateral speed, and sideslip angle. The algorithm is eventually tested based on the co-simulation of CarSim and MATLAB/Simulink. The results showed that the proposed robust adaptive UKF algorithm can significantly reduce the estimation error of three state variables, and is better than the standard accuracy and robustness of the UKF algorithm. This lays an important foundation for advanced driving assistance systems and precise automatic driving movement control.

Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model

In this study, a vehicle localization technique was employed to determine the required quantities in the identification of battery models by considering the behavior of multiple batteries instead of data from a single battery. In previous studies, a plant (e.g., a battery, motor, super-capacitor, or fuel cell) was identified based on a single piece of data. However, such an approach is disadvantageous in that it neglects the effect of process and measurement noise and assumes that the parameters obtained using data from a single plant are identical for all plants of the same type. First, deterministic parameter estimation (DPE), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) were initially applied to estimate the battery model parameters using data from a single battery. Second, a fusion-based approach was used to address the process and measurement noise problems through an adaptive unscented Kalman filter algorithm. With this approach, maximum likelihood estimation was employed to fuse multiple-battery data streams to enable the DPE, PSO, and TLBO to recalculate the model parameters based on filtered and fused quantities. A comparison between the experimental results and model outputs obtained using the aforementioned methods for parameter estimation indicated that the proposed multiple-battery approach enhances the accuracy of several identification methods. In contrast, it requires a high computational effort.

An Improved Adaptive Unscented Kalman Filter With Application in the Deeply Integrated BDS/INS Navigation System

The integration of the BeiDou Navigation Satellite System(BDS) and the Inertial Navigation System(INS) can provide a more reliable and accurate navigation service than either system alone. The deeply coupled architecture for BDS and INS integration has more superior performance than the loosely coupled or the tightly coupled. Owing to the complicated dynamic scenario and the nonlinear system's noise uncertainty, the adaptive Kalman filter(AKF) algorithm is often adopted in the deep integration(DI) system. The adaptive Sage window methods including innovation-based adaptive estimation(IAE) and residual-based adaptive estimation(RAE) are widely applied in AKF algorithms, but they have several limitations. We propose an improved adaptive unscented Kalman filter(AUKF) based on forgetting-factor-weight smoothing and multi-factor adaptation to overcome these limitations. Compared with the Sage window methods, the improved AUKF algorithm is immune to the quantity change of the satellites concerning integration and more sensitive to present dynamic. Furthermore, it can reduce the computation and storage burden in implementation. A simulation test based on a software platform and the deeply integrated BDS/INS navigation system is carried out to evaluate the performance of the improved AUKF. Simulation results show that the improved AUKF algorithm outperforms the extended Kalman filter(EKF) and has a similar performance with the RAE-AUKF.

Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression

To solve the problem of the inaccurate prediction on remaining useful life (RUL) for lithium-ion battery, we proposed an integrated algorithm which combines adaptive unscented kalman filter (AUKF) and genetic algorithm optimized support vector regression (GA-SVR). Firstly, the state space model with double exponential is established to describe the degradation of lithium battery. Then, the AUKF algorithm is introduced to update adaptively both the process noise covariance and the observation noise covariance. Next, the genetic algorithm is utilized to optimize the key parameters of SVR which realizes multi-step prediction. The effectiveness of the proposed method is verified by simulation experiments with NASA of battery dataset. Simulation results show that the proposed AUKF-GA-SVR achieves better prediction accuracy than existed methods such as unscented kalman filter, extended kalman filter, adaptive extended kalman filter (AEKF), adaptive unscented kalman filter, unscented kalman filter and relevance vector regression and AEKF-GA-SVR.

Lithium-ion Battery Modeling and State of Charge Estimation

State of charge (SOC) is an important indicator for guiding the charging-discharging operation of lithium-ion batteries. In this article, the equivalent circuit model of lithium-ion battery and the variable forgetting factor (VFF) least squares model identification method are proposed. This parameter identification method can improve the accuracy of the lithium-ion battery model, thereby ensuring the accuracy of the SOC estimation. Furthermore, based on the lithium-ion battery model, the adaptive unscented Kalman filter (AUKF) algorithm is proposed to estimate SOC of lithium-ion batteries. Experimental results show that the AUKF algorithm is good robustness, fast convergence, practicality and small error in SOC estimation of lithium-ion batteries. In conclusion, the VFF least squares model identification method and the AUKF algorithm are promising engineering application method.

A hybrid adaptive unscented Kalman filter algorithm

A hybrid adaptive unscented Kalman filter algorithm

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance.

The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability.